Knowledge ingestion
Your docs, FAQs, product catalogue and past tickets become the chatbot's training data, with automatic refresh as content changes.
Services
AI Assistants & RAG
Answers from your own knowledge, not the open internet
We build assistants on retrieval (RAG) over your documents, policies, tickets and databases — so your team or your customers get answers that cite where they came from, and a person when the answer isn't there.
- Answers grounded in your documents, with citations
- Internal knowledge search or customer-facing support
- Human handoff when the answer isn't there
- Web, Slack, WhatsApp and other channels
- Access that can follow your existing permissions
What this engagement covers
Scoped to your situation — we start with what you already have rather than a fixed package.
Multi-turn dialogue
Context-aware conversations that remember what the user said three messages ago, not just the last one.
Channel deployment
One bot deployed across your website, WhatsApp, Slack, Teams and SMS — same logic, native UX per platform.
Smart escalation
Confidence scoring routes uncertain queries to a human with the full conversation context, so the agent never starts cold.
Multilingual support
Language detection and responses in your customers' languages without maintaining a separate assistant per locale.
Usage review
Logs of questions, answers and handoffs, so you can see what is being asked and where the knowledge base has gaps.
Small first, then scale what works
Six stages, with a decision point after the pilot. Nothing grows until it has shown value.
01
Workflow Audit
Understand the current manual process, systems, users, inputs and outputs.
02
Architecture
Choose the smallest practical automation architecture.
03
Pilot
Build one usable workflow.
04
Integration
Connect APIs, browser workflows, internal tools, databases, AI models or third-party systems.
05
Validation
Test reliability, edge cases, user approval and expected output.
06
Scale
Expand only after the pilot demonstrates value.
What you get
- Assistant connected to your knowledge sources
- Deployment on the channels your team or customers use
- Conversation logs and review workflow
- Evaluation set of real questions to check answer quality
Tools we reach for
Chosen per project, not per habit. If your team already runs something that works, we build on it.
- Claude
- OpenAI
- LangGraph
- Vercel AI SDK
- Pinecone
- Redis
- WebSocket
- Next.js
FAQ
AI Assistants & RAG — common questions
It answers from your documents and cites them, so accuracy depends mostly on the quality and coverage of that content. We build a test set of real questions during the pilot and measure against it before launch.
Other things we build
Have a workflow in mind?
Tell us about the process. We'll review it and suggest the most practical next step.